One of the fundamental challenges in enterprise application integration is enabling smooth data flow between disparate applications. Data structure formats used by different applications are mostly unique to each application. Therefore, data mapping is often used in the integration process of transferring information between different applications. Data mapping is a process of transforming data from one application-specific format to another.
Conventional mapping techniques allow users to manually map data of one data structure to another data structure. Typical products available in the market that facilitate such mapping transformations are achieved using graphical mapping tools that allow the user to view the individual data attributes within each data structure. However, when large data structures are encountered with hundreds of attributes, the manual process can be very time-consuming and often frustrating.
Additionally, the manual mapping techniques, even when facilitated by graphical representations of each data structure, are subject to several disadvantages. One of the common disadvantages of conventional mapping techniques is the human involvement. Human intervention introduces the probability of human error in the mapping process. Human involvement also costs significant time and money. While some level of human involvement may present certain advantages, the human involvement in conventional mapping techniques is more demanding that it needs to be, especially for trivial mappings.
Another disadvantage of conventional mapping techniques is apparent in the creation of a reverse map. A reverse map is a compliment to a forward map. For example, where the forward map is intended to transform data from a first data structure into a usable form for a second data structure, the reverse map is intended to transform data from the second structure into a usable form for the first data structure (in the reverse direction). Unfortunately, conventional reverse data mapping techniques include starting the mapping all over again. In other words, after someone has manually created a forward map, they subsequently perform the manual process all over again in the reverse direction to create the reverse map for the same data structures.
Though the format of the data structures on disparate systems may be different, the names used for defining the attributes of each data structure are often the same or similar. For example, a customer record may have the name of the customer represented in one way or other. It could be split as ‘lastname, firstname’ or it may occur as ‘FullName’ or just as ‘Name’. In one way or another, though, the corresponding attributes can likely be found between the source and the destination data structures. The source data structure is the one to be transformed. The destination data structure is the one that is generated or populated with data from the source data structure.
Conventionally, a user visually scans both the source and the destination data structures (e.g. using a graphical mapping tool) and manually maps the data attributes from both data structures that the user wants to transfer. When dealing with data structures that have thousands of data attributes, this process can be alarmingly time consuming even if the mappings are for similarly or even identically named data attributes. This is because the conventional mapping techniques are dependent on manual intervention even for the most obvious transformations.
Another conventional mapping technique involves algorithms that perform mapping during runtime. In other words, data is transformed from one data format to another data format during runtime as needed. Such transformations do not actually refer to a map, but rather are performed individually according to an algorithm and a domain-based vocabulary dictionary. Unfortunately, the runtime mapping implementations do not take into account the specific features of various data structures. Instead, the runtime mapping implementations use a predefined set of terms and rules regarding how to treat different terms.
From the foregoing discussion, it should be apparent that a need exists for an apparatus, system, and method that overcome the limitations of conventional data mapping technologies. In particular, such an apparatus, system, and method would beneficially reduce the human involvement. Additionally, such an apparatus, system, and method would beneficially facilitate reverse mapping without requiring manual replication of the manual forward mapping operations.